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2,686 results for “Iron”
PARAGON 1 - KM2112 - Iron Uptake In Situ Measurements
<p>This dataset contains measurements of in situ rates of dissolved iron (Fe) uptake collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Rates of iron uptake were measured by subsampling 250 mL aliquots of whole seawater into polycarbonate bottles and spiking with a final concentration of 0.4 nmol L⁻¹ ⁵⁵FeCl₃ with a specific activity of 2.74 Ci mmol⁻¹ of Fe (Perkin Elmer). A killed blank measurement was also made by spiking an additional 250 mL aliquot with a final concentration of 1% glutaraldehyde prior to spiking with ⁵⁵FeCl₃. All aliquots were then incubated in the dark at in situ temperature for 8-10 hours. Incubations were terminated by filtering the entire 250 mL aliquot for each sample through a 0.2 µm polycarbonate filter. In order to remove extracellularly bound Fe, filters were rinsed 3 times with an oxalate wash according to Tang and Morel (2006) followed by three rinses with 0.2 µm filtered seawater. Filters were then transferred to high density polyethylene scintillation vials and submerged in 10 mL of Ultima Gold LLT scintillation cocktail (Perkin Elmer). Radioactivity incorporated into microbial biomass was measured on a TriCarb 4910TR scintillation counter. Values reported are blank-corrected averages of triplicate measurements from each sample but have not been adjusted for isotope dilution resulting from unlabeled iron present in situ. Timestamp is in UTC.</p>
PARAGON 1 - KM2112 - Iron Uptake Timecourse Incubations
<p>This dataset contains measurements of rates of dissolved iron (Fe) uptake collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from time-course incubation experiments initiated with whole seawater collected at 150 m using trace metal clean techniques and modified with various additions of organic carbon, iron, and/or nitrogen. Rates of iron uptake were measured at multiple time points for each experimental biological replicate by subsampling 250 mL aliquots from polycarbonate incubation bottles into new polycarbonate bottles and spiking with a final concentration of 0.4 nmol L⁻¹ ⁵⁵FeCl₃ with a specific activity of 2.74 Ci mmol⁻¹ of Fe (Perkin Elmer). A killed blank measurement was made for each experimental treatment by spiking an additional 250 mL aliquot with a final concentration of 1% glutaraldehyde prior to spiking with ⁵⁵FeCl₃. All aliquots were then incubated in the dark at in situ temperature for 8-10 hours. Incubations were terminated by filtering the entire 250 mL aliquot for each sample through a 0.2 µm polycarbonate filter. In order to remove extracellularly bound Fe, filters were rinsed 3 times with an oxalate wash according to Tang and Morel (2006) followed by three rinses with 0.2 µm filtered seawater. Filters were then transferred to high density polyethylene scintillation vials and submerged in 10 mL of Ultima Gold LLT scintillation cocktail (Perkin Elmer). Radioactivity incorporated into microbial biomass was measured on a TriCarb 4910TR scintillation counter. Values reported are blank-corrected but have not been adjusted for isotope dilution resulting from unlabeled iron present in situ. Timestamp is in UTC. Version 2 corrects formatting errors in the timestamp.</p>
PARAGON 2 - KM2209 - Iron Uptake Timecourse Incubations
<p>This dataset contains measurements of rates of dissolved iron (Fe) uptake collected during the PARAGON 2 expedition (KM2209) in the North Pacific Subtropical Gyre. Measurements come from time-course incubation experiments initiated with whole seawater collected at 150 m using trace metal clean techniques and modified with various additions of organic carbon, iron, and/or nitrogen. Rates of iron uptake were measured at multiple time points for each experimental biological replicate by subsampling 250 mL aliquots from polycarbonate incubation bottles into new polycarbonate bottles and spiking with a final concentration of 0.4 nmol L⁻¹ ⁵⁵FeCl₃ with a specific activity of 2.74 Ci mmol⁻¹ of Fe (Perkin Elmer). A killed blank measurement was made for each experimental treatment by spiking an additional 250 mL aliquot with a final concentration of 1% glutaraldehyde prior to spiking with ⁵⁵FeCl₃. All aliquots were then incubated in the dark at in situ temperature for 8-10 hours. Incubations were terminated by filtering the entire 250 mL aliquot for each sample through a 0.2 µm polycarbonate filter. In order to remove extracellularly bound Fe, filters were rinsed 3 times with an oxalate wash according to Tang and Morel (2006) followed by three rinses with 0.2 µm filtered seawater. Filters were then transferred to high density polyethylene scintillation vials and submerged in 10 mL of Ultima Gold LLT scintillation cocktail (Perkin Elmer). Radioactivity incorporated into microbial biomass was measured on a TriCarb 4910TR scintillation counter. Values reported are blank-corrected but have not been adjusted for isotope dilution resulting from unlabeled iron present in situ. Timestamp is in UTC.</p>
iSDAsoil: soil extractable Iron for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Iron (Fe) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.fe_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron mean value,</li> <li>sol_log.fe_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.fe_mehlich3 R-square: 0.817 Fitted values sd: 0.497 RMSE: 0.235 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.0165 -0.1312 -0.0082 0.1238 2.5077 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.913522 1.869721 2.093 0.036344 * regr.ranger 0.856893 0.007912 108.306 < 2e-16 *** regr.xgboost 0.027856 0.007738 3.600 0.000318 *** regr.cubist 0.146095 0.007230 20.207 < 2e-16 *** regr.nnet -0.879348 0.402810 -2.183 0.029037 * regr.cvglmnet 0.005610 0.004470 1.255 0.209415 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2349 on 57526 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.8173 F-statistic: 5.148e+04 on 5 and 57526 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.5. Pottery (A, C), animal bones (B), a human skull (C, D), and a flint tool (D) excavated from underneath the stone layer in Kaliszany (archaeological site no. 3)
<p>The set contains a figure, with with photographs that show examples of finds discovered during excavations at archaeological site 3 in Kaliszany, Wągrowiec commune, Poland. It is a stone and earth structure in which a hoard of metal objects dating to the Late Bronze Age was discovered in 1943. The photo is from the 2022 survey, when the south-western part of the structure was explored. <br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.1. Location of hoards mentioned in the text: white dots represent locations of hoards examined in the Biography of Hoards project; black dots represent locations of hoards examined in other multi-faceted projects
<p>The set contains a figure, with data, on the location of the hoards included (described in the related paper).<br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.3. Workflow in the Biography of Hoards project
<p>The set contains a figure and editable files associated with the figure.</p> <p>Figure presenting workflow of the project described in the related paper.</p> <p>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
WDXRF analysis of Iberian unfired potsherds from the Late Iron Age
<p>This data set contains 15 chemical analyses of unfired potsherds from the Iberian workshop of the Mas de Moreno (Teruel, Spain). The chemical composition of the samples was obtained by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF).</p> <p><strong>Method</strong></p> <p>The outer surfaces of all the samples were mechanically removed prior to analysis.</p> <p>The samples were heated to 950°C for one hour after 24h drying at 50°C, weighted for LOI calculation and ground in a tungsten carbide mortar. 0.8 g of powder was mixed with 3.2 g of Spectroflux 110 flux (Johnson Matthey; 33.5% metaborate and 66.5% lithium tetraborate) and melted in gold-platinum crucibles with an autofluxer melting device (Breitländer).</p> <p>The data were collected with a SRS 3400 (Bruker) spectrometer of 3 kW power (working at 60 kV, 100 mA max.). The spectrometer was equipped with a rhodium tube window, four analyser crystals (OVO-55, LiF200, LiF220, PET), two collimators (0.46° and 0.15°) and two sensors (a proportional Ar/CH4 gas-flow counter and a scintillation counter). The calibration of the instrument was carried out on 40 international certified reference materials.</p> <p><strong>Data description</strong></p> <ul> <li><em>sample</em>: sample reference.</li> <li><em>date</em>: date of the analysis.</li> <li><em>laboratory</em>: analysis laboratory.</li> <li><em>stratigraphy</em>: stratigraphic unit (all dated to the first half of the 1<sup>st</sup> century BC).</li> <li><em>artefact</em>: typology.</li> <li><em>part</em>: analysed part of the artefact.</li> <li><em>LOI</em>: loss on ignition (percent).</li> <li><em>CaO</em>, <em>Fe<sub>2</sub>O<sub>3</sub></em>, <em>TiO<sub>2</sub></em> , <em>K<sub>2</sub>O</em>, <em>SiO<sub>2</sub></em> , <em>Al<sub>2</sub>O<sub>3</sub></em> , <em>MgO</em>, <em>MnO</em>, <em>Na<sub>2</sub>O</em>, <em>P<sub>2</sub>O<sub>5</sub></em>: oxide mass percents.</li> <li><em>Zr</em>, <em>Sr</em>, <em>Rb</em>, <em>Zn</em>, <em>Cr</em>, <em>Ni</em>, <em>La</em>, <em>Ba</em>, <em>V</em>, <em>Ce</em>, <em>Y</em>, <em>Th</em>, <em>Pb</em>, <em>Cu</em>: ppm.</li> </ul> <p>Data below the following limits should be considered unreliable:</p> <ul> <li><em>Na<sub>2</sub>O</em>: 0.5 %</li> <li><em>La</em>: 24 ppm</li> <li><em>Y</em>: 15 ppm</li> <li><em>Th</em>: 15 ppm</li> <li><em>Pb</em>: 20 ppm</li> <li><em>Cu</em>: 10 ppm</li> </ul>
WDXRF analysis of Iberian potsherds from the Late Iron Age
<p>This data set contains 99 chemical analyses of ceramic potsherds from the Iberian workshop of the Mas de Moreno (Teruel, Spain). The chemical composition of the samples was obtained by wavelength-dispersive X-ray fluorescence spectrometry (WDXRF).</p> <p><strong>Method</strong></p> <p>The outer surfaces of all the samples were mechanically removed prior to analysis.</p> <p>The samples were heated to 950°C for one hour after 24h drying at 50°C, weighted for LOI calculation and ground in a tungsten carbide mortar. 0.8 g of powder was mixed with 3.2 g of Spectroflux 110 flux (Johnson Matthey; 33.5% metaborate and 66.5% lithium tetraborate) and melted in gold-platinum crucibles with an autofluxer melting device (Breitländer).</p> <p>The data were collected with a SRS 3400 (Bruker) spectrometer of 3 kW power (working at 60 kV, 100 mA max.). The spectrometer was equipped with a rhodium tube window, four analyser crystals (OVO-55, LiF200, LiF220, PET), two collimators (0.46° and 0.15°) and two sensors (a proportional Ar/CH4 gas-flow counter and a scintillation counter). The calibration of the instrument was carried out on 40 international certified reference materials.</p> <p><strong>Data description</strong></p> <ul> <li><em>sample</em>: sample reference.</li> <li><em>date</em>: date of the analysis.</li> <li><em>laboratory</em>: analysis laboratory.</li> <li><em>stratigraphy</em>: stratigraphic unit.</li> <li><em>artefact</em>: typology.</li> <li><em>part</em>: analysed part of the artefact.</li> <li><em>decoration</em>: did the sampled artefact carry a painted decoration?</li> <li><em>LOI</em>: loss on ignition (percent).</li> <li><em>CaO</em>, <em>Fe<sub>2</sub>O<sub>3</sub></em>, <em>TiO<sub>2</sub></em> , <em>K<sub>2</sub>O</em>, <em>SiO<sub>2</sub></em> , <em>Al<sub>2</sub>O<sub>3</sub></em> , <em>MgO</em>, <em>MnO</em>, <em>Na<sub>2</sub>O</em>, <em>P<sub>2</sub>O<sub>5</sub></em>: oxide mass percents.</li> <li><em>Zr</em>, <em>Sr</em>, <em>Rb</em>, <em>Zn</em>, <em>Cr</em>, <em>Ni</em>, <em>La</em>, <em>Ba</em>, <em>V</em>, <em>Ce</em>, <em>Y</em>, <em>Th</em>, <em>Pb</em>, <em>Cu</em>: ppm.</li> </ul> <p>Data below the following limits should be considered unreliable:</p> <ul> <li><em>Na<sub>2</sub>O</em>: 0.5 %</li> <li><em>La</em>: 24 ppm</li> <li><em>Y</em>: 15 ppm</li> <li><em>Th</em>: 15 ppm</li> <li><em>Pb</em>: 20 ppm</li> <li><em>Cu</em>: 10 ppm</li> </ul>
Animal bones from Iron Age settlements in Scania, Southern Sweden
<p>This data is a compilation of the zooarchaeological record from Iron Age settlements in Scania, southern Sweden. It consists of data from various technical reports produced between 1961 to 2019, by different analysts. Published reports and unpublished but archived communications are included. This data may be of interest to anyone interested in archaeological themes involving animals in any kind, such as economy, animal husbandry, animal production, hunting, fishing, and so on. It may also be of paleozoological interest, as it contains valuable fauna historical information such as presence of wild species of different kinds. </p> <p>The database is the basis for the published catalogue included in the book "Animal husbandry in Iron Age Scania, with a catalogue" published 2022. The book is open acess and you can download it via this link: https://www.ht.lu.se/en/series/9128370/</p> <p>The data can bee accessed through a one .csv-file, which is an export of the data set which was originally recorded in a MS Access-database. Both files are published in this version. The dataset consists of data on 130 animal bone assemblages from 101 Scanian settlement sites.</p> <p>The original Access-database, with two levels, one (Site) with descriptive information on the archaeological site (totally 12 variables), and one (zooarch-overview) with quantitative data on number of specimens, in general and per recorded taxa (totally 35 variables). Presence of bird, fish, amphibian and wild mammalian taxa is also included. </p> <p>Included is a READ ME (.csv) describing the data set in more detail.</p> <p>ERRATA (READ ME-file): No of observations is 130, not 131.</p>
XRD analysis of Iberian unfired potsherds from the Late Iron Age
<p>This dataset contains 11 mineralogical analyses of ceramic potsherds by powder X-ray diffraction (XRD). The samples come from the Iberian workshop of the Mas de Moreno (Teruel, Spain).</p> <p><strong>Method</strong></p> <p>The outer surfaces of all the samples were mechanically removed prior to analysis. All samples were manualy powdered in an agate mortar.</p> <p>The data were collected with a D8 Advance (Bruker) diffractometer in Bragg-Brentano configuration working at 1.6 kW (40 kV, 40 mA) and equipped with a copper anode source (kα1 = 1.5406 ; the kβ ray being removed by a Ni-filter in the diffracted beam). An 8 mm anti-scattering slit was mounted in front of the LynxEye© CCD detector. The explored area covered the 3-70° (2θ) range, with an angle step of 0.02° and a time step of 2 seconds. The stability of the instrument was checked between the different series of measurements by analyzing a standard (corundum crystal, NIST 1976).</p> <p><strong>Data description</strong></p> <p>File format: Bruker raw.</p> <p>The "XRD_clay_raw.zip" archive contains the raw diffractograms.</p> <p>All file names start with the sample code (a code starting with "BDX" followed by a 5-digit number), followed by a capital "P" (for powder diffraction).</p> <p>The "XRD_clay_raw.csv" file contains all results expressed in counts, one sample per row. The first column gives the angular position (2 thêta).</p>
XRD analysis of Iberian potsherds from the Late Iron Age
<p>This data set contains 76 mineralogical analyses of ceramic potsherds by powder X-ray diffraction (XRD). The samples come from the Iberian workshop of the Mas de Moreno (Teruel, Spain) and the settlements of Torre Cremada (Valdeltormo, Teruel) and El Palao (Alcañiz, Teruel).</p> <p><strong>Method</strong></p> <p>The outer surfaces of all the samples were mechanically removed prior to analysis. All samples were manualy powdered in an agate mortar.</p> <p>The data were collected with a D8 Advance (Bruker) diffractometer in Bragg-Brentano configuration working at 1.6 kW (40 kV, 40 mA) and equipped with a copper anode source (k<sub>α1</sub> = 1.5406 ; the k<sub>β</sub> ray being removed by a Ni-filter in the diffracted beam). An 8 mm anti-scattering slit was mounted in front of the LynxEye© CCD detector. The explored area covered the 3-70° (2θ) range, with an angle step of 0.02° and a time step of 2 seconds. The stability of the instrument was checked between the different series of measurements by analyzing a standard (corundum crystal, NIST 1976).</p> <p><strong>Data description</strong></p> <p>File format: Bruker raw.</p> <p>The "XRD_ceramic_raw.zip" archive contains the raw diffractograms.</p> <p>All file names start with the sample code (a code starting with "BDX" followed by a 5-digit number), followed by a capital "P" (for powder diffraction).</p> <p>The "XRD_ceramic_raw.csv" file contains all results expressed in counts, one sample per column. The first column gives the angular position (2 thêta).</p>
Dataset for Precursor Nuclearity and Ligand Effects in Atomically-Dispersed Heterogeneous Iron Catalysts for Alkyne Semi-Hydrogenation
<p>This dataset complements the publication entitled "Precursor Nuclearity and Ligand Effects in Atomically-Dispersed Heterogeneous Iron Catalysts for Alkyne Semi-Hydrogenation" by Dario Faust Akl, Andrea Ruiz-Ferrando, Dr. Edvin Fako, Dr. Roland Hauert, Dr. Olga Safonova, Dr. Sharon Mitchell, Prof. Núria López, Prof. Javier Pérez-Ramírez. Please refer to the Readme.txt file for information about the file structure and content.<br> </p>
Iron in Antarctic sea ice
<p>This dataset contains an updated compilation of pan-Antarctic dissolved, total dissolvable and particulate iron concentrations from both landfast and pack sea ice collected at 64 ice stations during eleven expeditions between 2000 and 2016 during different seasons.</p><p>It includes an .xlsx file and a .csv file, both containing the metadata with also the references to the original published datasets.</p><p><strong>Any use of the data in this document should also refer to the original dataset and authors.</strong></p>
Assessment of dissolved iron in Iowa lakes
This study assessed the abundance of dissolved iron (DFe) in Iowa’s lakes. The micronutrient iron has been noted to play a crucial role in regulating phytoplankton growth, however most studies have focused on large lakes with persistent phytoplankton blooms that are known to undergo iron limitation, such as Lake Erie. There are few datasets of DFe in lakes, especially those that are smaller and susceptible to phytoplankton blooms. In order to assess the spatial distribution of DFe in lakes throughout Iowa, this study obtained DFe measurements over a suite of recreational lakes over a summer season in 2018. Weekly monitoring of DFe (for 15 weeks) was conducted to assess temporal trends.
ACF database on the vitamin A and iron outcomes from the MANGO trial
<p>This dataset contains the variables used in the analysis of the vitamin A and iron outcomes of the MANGO trial carried out in Burkina Faso between 2016 and 2018. </p>
ds-uct-001: Cast Iron GGG40: X-Ray micro-CT of a nodular cast iron sample class GGG40.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of a nodular cast iron sample class GGG40, including both raw projection data and the final reconstructions, for three different resolutions (voxel sizes of 1 μm, 3 μm and 11 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1 (1024) - Voxel size: 1 μm; Sample-source: 26 mm; Sample-detector: 150 mm; Optical magnification: 4.0X; Filter: HE#6; Beam energy: 160 kV; Power: 10 W; Exposure time: 60.0 sec; Projections: 1600.<br> .Tomo2 (1024) - Voxel size: 3 μm; Sample-source: 28 mm; Sample-detector: 35 mm; Optical magnification: 4.0X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 10.0 sec; Projections: 3200.<br> .Tomo3 (1024) - Voxel size: 11 μm; Sample-source: 30 mm; Sample-detector: 158 mm; Optical magnification: 0.4X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 3.0 sec; Projections: 3200.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-001.txt<br> .ds-uct-001_cast_iron_ggg40_01um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_03um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_11um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_01um_1600p.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_recon.txm</p>
Effect of superparamagnetic iron oxide nanoparticles on glucose homeostasis on type 2 diabetes experimental model
<p>The data correspond to figures in the paper by Ali, L.M.A. et al. Life Sciences 245 (2020) 117361. doi:10.1016/j.lfs.2020.117361.</p>
Global physical input-output tables for iron and steel (2008-2017).
<p><strong>Dataset:</strong> Global physical input-output tables for iron and steel</p> <p><strong>Years:</strong> 2008-2017</p> <p><strong>Base classification:</strong> 32 regions, 39 processes and 30 flows</p> <p><strong>Associated journal article: </strong>The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology)</p> <p><strong>Associated GitHub repository</strong>: www.github.com/fineprint-global/PIOLab</p> <p><strong>Contact:</strong> hanspeter.wieland@wu.ac.at</p> <p>The folder <em>RawData</em> contains the unprocessed results of the reconciliation run in the PIOLab. These tables (in the Tvy format) form the basis for the R scripts that are available from the GitHub repository mentioned above. Please note the instructions on GitHub for further information and how i.e. where the content of <em>RawData</em> needs to be stored in your local repository.</p> <p>The folder <em>gPSUT</em> contains the processed physical supply-use tables, including final use matrices and boundary input and output blocks. The variable names are described in detail in the method section of the journal article.</p> <p>The folder <em>gPIOT</em> contains the process-by-process IO model, which was used for the calculation of the footprint indicators in the Journal article. Please read the information on the footprint calculus in the journal article.</p> <p>The folder<em> Diagnostics </em>contains, for all years of the time series, results from the analyses of the constraint realization. The journal article presents only the diagnostic test for the year 2008.</p>
Shock Ramp Compressions Measurements of Iron on the Sandia National Laboratories' Z-Machine
<p>This data contains 1) the apparent velocity data from Velocity Interferometer System for Any Reflector (VISAR) data analyzed using the PointVISAR program for experiments Z3155 and Z3339 and 2) the equation of state results from analyzing the velocity data using a backward integration -- forward Lagrangian analysis.<br> These experiments were performed on the Sandia National Laboratories' Z-Machine, where the iron samples were dynamically compressed via shocked compression to approximately 275 Gpa and further ramp compression to approximately 400 GPa. This covers pressure-temperature regions near the melt line as well as the interior conditions of terrestrial planets.<br> The Z3155 data include four samples, each with two VISAR traces, and the Z3339 data include six samples, each with two or three VISAR traces.<br> The apparent velocity can be corrected to true velocity using the latest lithium fluoride window correction for a 532 nm wavelength.<br> PointVISAR is available as part of the Sandia Matlab AnalysiS Hierarchy (SMASH) toolbox.<br> Details of the backward integration -- forward Lagrangian anaylsis that was used can be found in the related publication.</p> <p>Example data file interpretation: "Z3155_north_panel_bot_sample_01.txt" is the first VISAR trace from the bottom sample of the north panel on experiment Z3155.<br> "Z3155_EoS_combined.txt" is the sample-averaged Equation of State result from experiment Z3155.</p> <p>Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13961 O</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.